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        <span>样本数据的生成</span>
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                <a href="/2020/07/27/python%20work/%E6%A0%B7%E6%9C%AC%E6%95%B0%E6%8D%AE%E7%9A%84%E7%94%9F%E6%88%90/">样本数据的生成</a>
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        <h1 id="样本数据的生成"><a href="#样本数据的生成" class="headerlink" title="样本数据的生成"></a>样本数据的生成</h1><p>样本数据只有222个，其中不活跃状态的197个，一般状态的18个，活跃状态的7个，样本数据太不活跃，所以需要将样本进行生成。</p>
<p>初步打算：不活跃的不变</p>
<p>一般状态的增加180个，</p>
<p>活跃状态的增加190个</p>
<p>利用random生成</p>
<p><a target="_blank" rel="noopener" href="https://www.jb51.net/article/152731.htm">https://www.jb51.net/article/152731.htm</a></p>
<p>标注差在进行变换，所以分类个数永远不是一个定值</p>
<p>先随机生成数据</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> random</span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">data = pd.read_excel(<span class="string">&#x27;data1&#x27;</span> + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">data.fillna(<span class="number">0</span>, inplace=<span class="literal">True</span>)</span><br><span class="line"><span class="comment"># 转换列表</span></span><br><span class="line">list_data = np.array(data).tolist()</span><br><span class="line">list_y1 = [<span class="built_in">int</span>(list_data[i][<span class="number">2</span>]) <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data))]</span><br><span class="line">y1_mean = np.array(list_y1).mean()</span><br><span class="line">y1_std = np.array(list_y1).std()</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">14</span>*<span class="number">200</span>):</span><br><span class="line">    list_y1.append(random.randint(<span class="number">0</span>, <span class="number">10</span>))</span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">14</span>*<span class="number">50</span>):</span><br><span class="line">    list_y1.append(random.randint(<span class="number">3</span>, <span class="number">25</span>))</span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">14</span>*<span class="number">50</span>):</span><br><span class="line">    list_y1.append(random.randint(<span class="number">15</span>, <span class="number">50</span>))</span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">14</span>*<span class="number">50</span>):</span><br><span class="line">    list_y1.append(random.randint(<span class="number">35</span>, <span class="number">120</span>))</span><br></pre></td></tr></table></figure>

<p>状态查看</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br></pre></td><td class="code"><pre><span class="line">list_z_y = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">int</span>(<span class="built_in">len</span>(list_y1)/<span class="number">14</span>)):</span><br><span class="line">    y1 = []</span><br><span class="line">    <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(list_y1)):</span><br><span class="line">        <span class="keyword">if</span> <span class="number">14</span> * i &lt;= j &lt; <span class="number">14</span> * (i + <span class="number">1</span>):</span><br><span class="line">            y1.append(list_y1[j])</span><br><span class="line">    list_z_y.append(np.array(y1).mean())</span><br><span class="line">Y_Z = []</span><br><span class="line">num1 = <span class="number">0</span></span><br><span class="line">num2 = <span class="number">0</span></span><br><span class="line">num3 = <span class="number">0</span></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(list_z_y)):</span><br><span class="line">    <span class="keyword">if</span> list_z_y[i] &lt; <span class="number">1</span>:</span><br><span class="line">        Y_Z.append(<span class="number">0</span>)</span><br><span class="line">        num1 += <span class="number">1</span></span><br><span class="line">    <span class="keyword">elif</span> <span class="number">1</span> &lt;= list_z_y[i] &lt; np.array(list_y1).std():</span><br><span class="line">        Y_Z.append(<span class="number">1</span>)</span><br><span class="line">        num2 += <span class="number">1</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        Y_Z.append(<span class="number">2</span>)</span><br><span class="line">        num3 += <span class="number">1</span></span><br><span class="line"><span class="number">197</span>:<span class="number">273</span>:<span class="number">102</span></span><br></pre></td></tr></table></figure>

<p>基于原本的数据看下规律</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># 将Y_Z变成字典形式</span></span><br><span class="line">xuhao = np.arange(<span class="built_in">len</span>(Y_Z)).tolist()</span><br><span class="line">dict_Y_Z = <span class="built_in">dict</span>(<span class="built_in">zip</span>(xuhao, Y_Z))</span><br><span class="line">sort_dict = <span class="built_in">sorted</span>(<span class="built_in">zip</span>(dict_Y_Z.values(), dict_Y_Z.keys()))</span><br></pre></td></tr></table></figure>

<p>对原本的数据进行排序</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br></pre></td><td class="code"><pre><span class="line">[(<span class="number">0</span>, <span class="number">217</span>),</span><br><span class="line"> (<span class="number">0</span>, <span class="number">218</span>),</span><br><span class="line"> (<span class="number">0</span>, <span class="number">219</span>),</span><br><span class="line"> (<span class="number">0</span>, <span class="number">220</span>),</span><br><span class="line"> (<span class="number">0</span>, <span class="number">221</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">10</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">17</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">33</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">37</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">44</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">70</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">75</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">85</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">91</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">98</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">103</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">107</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">142</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">146</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">152</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">179</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">181</span>),</span><br><span class="line"> (<span class="number">1</span>, <span class="number">209</span>),</span><br><span class="line"> (<span class="number">2</span>, <span class="number">54</span>),</span><br><span class="line"> (<span class="number">2</span>, <span class="number">129</span>),</span><br><span class="line"> (<span class="number">2</span>, <span class="number">144</span>),</span><br><span class="line"> (<span class="number">2</span>, <span class="number">158</span>),</span><br><span class="line"> (<span class="number">2</span>, <span class="number">172</span>),</span><br><span class="line"> (<span class="number">2</span>, <span class="number">198</span>),</span><br><span class="line"> (<span class="number">2</span>, <span class="number">206</span>)</span><br></pre></td></tr></table></figure>

<p>查看下对应状态下的x1,x2,x3,x4的变化</p>
<p>数据10</p>
<table>
<thead>
<tr>
<th>11</th>
<th>1</th>
<th>2</th>
<th>799</th>
<th>520</th>
<th>3</th>
<th>3</th>
<th>3</th>
<th>37</th>
</tr>
</thead>
<tbody><tr>
<td>11</td>
<td>2</td>
<td>4</td>
<td>645</td>
<td>533</td>
<td>5</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>11</td>
<td>3</td>
<td>1</td>
<td>659</td>
<td>651</td>
<td>2</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>11</td>
<td>4</td>
<td>0</td>
<td>989</td>
<td>916</td>
<td>3</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>11</td>
<td>5</td>
<td>5</td>
<td>817</td>
<td>442</td>
<td>0</td>
<td>3</td>
<td></td>
<td></td>
</tr>
<tr>
<td>11</td>
<td>6</td>
<td>6</td>
<td>753</td>
<td>350</td>
<td>1</td>
<td>4</td>
<td></td>
<td></td>
</tr>
<tr>
<td>11</td>
<td>7</td>
<td>2</td>
<td>745</td>
<td>626</td>
<td>1</td>
<td>5</td>
<td></td>
<td></td>
</tr>
<tr>
<td>11</td>
<td>8</td>
<td>3</td>
<td>807</td>
<td>503</td>
<td>0</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>11</td>
<td>9</td>
<td>3</td>
<td>774</td>
<td>304</td>
<td>2</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>11</td>
<td>10</td>
<td>3</td>
<td>1265</td>
<td>294</td>
<td>0</td>
<td>4</td>
<td></td>
<td></td>
</tr>
<tr>
<td>11</td>
<td>11</td>
<td>5</td>
<td>668</td>
<td>36</td>
<td>0</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>11</td>
<td>12</td>
<td>4</td>
<td>1075</td>
<td>472</td>
<td>1</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>11</td>
<td>13</td>
<td>5</td>
<td>1420</td>
<td>372</td>
<td>1</td>
<td>3</td>
<td></td>
<td></td>
</tr>
<tr>
<td>11</td>
<td>14</td>
<td>4</td>
<td>912</td>
<td>320</td>
<td>1</td>
<td>4</td>
<td></td>
<td></td>
</tr>
</tbody></table>
<table>
<thead>
<tr>
<th>18</th>
<th>1</th>
<th>3</th>
<th>26</th>
<th>144</th>
<th>2</th>
<th>4</th>
<th>2</th>
<th>18</th>
</tr>
</thead>
<tbody><tr>
<td>18</td>
<td>2</td>
<td>4</td>
<td>24</td>
<td>95</td>
<td>2</td>
<td>3</td>
<td></td>
<td></td>
</tr>
<tr>
<td>18</td>
<td>3</td>
<td>2</td>
<td>16</td>
<td>107</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>18</td>
<td>4</td>
<td>5</td>
<td>14</td>
<td>76</td>
<td>2</td>
<td>3</td>
<td></td>
<td></td>
</tr>
<tr>
<td>18</td>
<td>5</td>
<td>7</td>
<td>32</td>
<td>398</td>
<td>0</td>
<td>5</td>
<td></td>
<td></td>
</tr>
<tr>
<td>18</td>
<td>6</td>
<td>4</td>
<td>11</td>
<td>91</td>
<td>0</td>
<td>3</td>
<td></td>
<td></td>
</tr>
<tr>
<td>18</td>
<td>7</td>
<td>1</td>
<td>13</td>
<td>35</td>
<td>0</td>
<td>3</td>
<td></td>
<td></td>
</tr>
<tr>
<td>18</td>
<td>8</td>
<td>1</td>
<td>29</td>
<td>144</td>
<td>0</td>
<td>0</td>
<td></td>
<td></td>
</tr>
<tr>
<td>18</td>
<td>9</td>
<td>4</td>
<td>12</td>
<td>100</td>
<td>0</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>18</td>
<td>10</td>
<td>4</td>
<td>12</td>
<td>65</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>18</td>
<td>11</td>
<td>3</td>
<td>19</td>
<td>41</td>
<td>0</td>
<td>4</td>
<td></td>
<td></td>
</tr>
<tr>
<td>18</td>
<td>12</td>
<td>2</td>
<td>10</td>
<td>47</td>
<td>1</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>18</td>
<td>13</td>
<td>3</td>
<td>46</td>
<td>46</td>
<td>1</td>
<td>3</td>
<td></td>
<td></td>
</tr>
</tbody></table>
<table>
<thead>
<tr>
<th>34</th>
<th>1</th>
<th>1</th>
<th>5757</th>
<th>3368</th>
<th>5</th>
<th>0</th>
<th>1</th>
<th>99</th>
</tr>
</thead>
<tbody><tr>
<td>34</td>
<td>2</td>
<td>1</td>
<td>8469</td>
<td>4823</td>
<td>4</td>
<td>0</td>
<td></td>
<td></td>
</tr>
<tr>
<td>34</td>
<td>3</td>
<td>1</td>
<td>8290</td>
<td>5837</td>
<td>1</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>34</td>
<td>4</td>
<td>3</td>
<td>4158</td>
<td>5776</td>
<td>2</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>34</td>
<td>5</td>
<td>2</td>
<td>2713</td>
<td>10177</td>
<td>6</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>34</td>
<td>6</td>
<td>2</td>
<td>2351</td>
<td>6625</td>
<td>1</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>34</td>
<td>7</td>
<td>3</td>
<td>1679</td>
<td>3366</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>34</td>
<td>8</td>
<td>1</td>
<td>1847</td>
<td>3648</td>
<td>5</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>34</td>
<td>9</td>
<td>2</td>
<td>3032</td>
<td>3022</td>
<td>1</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>34</td>
<td>10</td>
<td>8</td>
<td>2006</td>
<td>2113</td>
<td>3</td>
<td>3</td>
<td></td>
<td></td>
</tr>
<tr>
<td>34</td>
<td>11</td>
<td>4</td>
<td>1700</td>
<td>2165</td>
<td>2</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>34</td>
<td>12</td>
<td>8</td>
<td>1179</td>
<td>1162</td>
<td>2</td>
<td>3</td>
<td></td>
<td></td>
</tr>
<tr>
<td>34</td>
<td>13</td>
<td>6</td>
<td>1970</td>
<td>1484</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>34</td>
<td>14</td>
<td>4</td>
<td>1828</td>
<td>1784</td>
<td>2</td>
<td>2</td>
<td></td>
<td></td>
</tr>
</tbody></table>
<table>
<thead>
<tr>
<th>38</th>
<th>1</th>
<th>2</th>
<th>891</th>
<th>5400</th>
<th>1</th>
<th>1</th>
<th>0</th>
<th>99</th>
</tr>
</thead>
<tbody><tr>
<td>38</td>
<td>2</td>
<td>6</td>
<td>281</td>
<td>447</td>
<td>0</td>
<td>3</td>
<td></td>
<td></td>
</tr>
<tr>
<td>38</td>
<td>3</td>
<td>2</td>
<td>316</td>
<td>745</td>
<td>0</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>38</td>
<td>4</td>
<td>9</td>
<td>737</td>
<td>630</td>
<td>0</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>38</td>
<td>5</td>
<td>4</td>
<td>191</td>
<td>467</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>38</td>
<td>6</td>
<td>1</td>
<td>250</td>
<td>510</td>
<td>0</td>
<td>0</td>
<td></td>
<td></td>
</tr>
<tr>
<td>38</td>
<td>7</td>
<td>1</td>
<td>202</td>
<td>487</td>
<td>1</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>38</td>
<td>8</td>
<td>0</td>
<td>322</td>
<td>530</td>
<td>1</td>
<td>0</td>
<td></td>
<td></td>
</tr>
<tr>
<td>38</td>
<td>9</td>
<td>7</td>
<td>352</td>
<td>584</td>
<td>0</td>
<td>3</td>
<td></td>
<td></td>
</tr>
<tr>
<td>38</td>
<td>10</td>
<td>6</td>
<td>250</td>
<td>416</td>
<td>1</td>
<td>4</td>
<td></td>
<td></td>
</tr>
<tr>
<td>38</td>
<td>11</td>
<td>3</td>
<td>364</td>
<td>418</td>
<td>1</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>38</td>
<td>12</td>
<td>1</td>
<td>382</td>
<td>632</td>
<td>1</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>38</td>
<td>13</td>
<td>10</td>
<td>448</td>
<td>482</td>
<td>0</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>38</td>
<td>14</td>
<td>10</td>
<td>461</td>
<td>1009</td>
<td>1</td>
<td>4</td>
<td></td>
<td></td>
</tr>
</tbody></table>
<table>
<thead>
<tr>
<th>45</th>
<th>1</th>
<th>2</th>
<th>2901</th>
<th>26</th>
<th>0</th>
<th>2</th>
<th>2</th>
<th>19</th>
</tr>
</thead>
<tbody><tr>
<td>45</td>
<td>2</td>
<td>0</td>
<td>4919</td>
<td>20</td>
<td>0</td>
<td>0</td>
<td></td>
<td></td>
</tr>
<tr>
<td>45</td>
<td>3</td>
<td>1</td>
<td>4489</td>
<td>21</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>45</td>
<td>4</td>
<td>3</td>
<td>1614</td>
<td>20</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>45</td>
<td>5</td>
<td>3</td>
<td>442</td>
<td>13</td>
<td>0</td>
<td>3</td>
<td></td>
<td></td>
</tr>
<tr>
<td>45</td>
<td>6</td>
<td>2</td>
<td>703</td>
<td>13</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>45</td>
<td>7</td>
<td>3</td>
<td>130</td>
<td>13</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>45</td>
<td>8</td>
<td>1</td>
<td>108</td>
<td>15</td>
<td>0</td>
<td>0</td>
<td></td>
<td></td>
</tr>
<tr>
<td>45</td>
<td>9</td>
<td>2</td>
<td>215</td>
<td>14</td>
<td>0</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>45</td>
<td>10</td>
<td>2</td>
<td>141</td>
<td>16</td>
<td>0</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>45</td>
<td>11</td>
<td>3</td>
<td>476</td>
<td>17</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>45</td>
<td>12</td>
<td>6</td>
<td>230</td>
<td>5</td>
<td>0</td>
<td>5</td>
<td></td>
<td></td>
</tr>
<tr>
<td>45</td>
<td>13</td>
<td>2</td>
<td>378</td>
<td>18</td>
<td>0</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>45</td>
<td>14</td>
<td>1</td>
<td>377</td>
<td>13</td>
<td>0</td>
<td>0</td>
<td></td>
<td></td>
</tr>
</tbody></table>
<table>
<thead>
<tr>
<th>71</th>
<th>1</th>
<th>1</th>
<th>29</th>
<th>17</th>
<th>1</th>
<th>1</th>
<th>0</th>
<th>18</th>
</tr>
</thead>
<tbody><tr>
<td>71</td>
<td>2</td>
<td>0</td>
<td>4</td>
<td>9</td>
<td>0</td>
<td>0</td>
<td></td>
<td></td>
</tr>
<tr>
<td>71</td>
<td>3</td>
<td>0</td>
<td>0</td>
<td>12</td>
<td>1</td>
<td>0</td>
<td></td>
<td></td>
</tr>
<tr>
<td>71</td>
<td>4</td>
<td>0</td>
<td>449</td>
<td>7</td>
<td>0</td>
<td>0</td>
<td></td>
<td></td>
</tr>
<tr>
<td>71</td>
<td>5</td>
<td>3</td>
<td>122</td>
<td>12</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>71</td>
<td>6</td>
<td>0</td>
<td>67</td>
<td>10</td>
<td>0</td>
<td>0</td>
<td></td>
<td></td>
</tr>
<tr>
<td>71</td>
<td>7</td>
<td>2</td>
<td>57</td>
<td>8</td>
<td>1</td>
<td>2</td>
<td></td>
<td></td>
</tr>
<tr>
<td>71</td>
<td>8</td>
<td>1</td>
<td>9</td>
<td>10</td>
<td>0</td>
<td>0</td>
<td></td>
<td></td>
</tr>
<tr>
<td>71</td>
<td>9</td>
<td>1</td>
<td>2</td>
<td>9</td>
<td>2</td>
<td>0</td>
<td></td>
<td></td>
</tr>
<tr>
<td>71</td>
<td>10</td>
<td>2</td>
<td>280</td>
<td>8</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>71</td>
<td>11</td>
<td>4</td>
<td>128</td>
<td>7</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>71</td>
<td>12</td>
<td>1</td>
<td>35</td>
<td>8</td>
<td>2</td>
<td>0</td>
<td></td>
<td></td>
</tr>
<tr>
<td>71</td>
<td>13</td>
<td>1</td>
<td>143</td>
<td>10</td>
<td>0</td>
<td>1</td>
<td></td>
<td></td>
</tr>
<tr>
<td>71</td>
<td>14</td>
<td>0</td>
<td>181</td>
<td>12</td>
<td>0</td>
<td>0</td>
<td></td>
<td></td>
</tr>
</tbody></table>
<p>状态为活跃时</p>
<table>
<thead>
<tr>
<th>207</th>
<th>1</th>
<th>57</th>
<th>761</th>
<th>4429</th>
<th>0</th>
<th>42</th>
<th>0</th>
<th>3</th>
</tr>
</thead>
<tbody><tr>
<td>207</td>
<td>2</td>
<td>85</td>
<td>109</td>
<td>497</td>
<td>0</td>
<td>68</td>
<td></td>
<td></td>
</tr>
<tr>
<td>207</td>
<td>3</td>
<td>78</td>
<td>119</td>
<td>439</td>
<td>0</td>
<td>71</td>
<td></td>
<td></td>
</tr>
<tr>
<td>207</td>
<td>4</td>
<td>96</td>
<td>173</td>
<td>447</td>
<td>0</td>
<td>83</td>
<td></td>
<td></td>
</tr>
<tr>
<td>207</td>
<td>5</td>
<td>86</td>
<td>152</td>
<td>430</td>
<td>0</td>
<td>70</td>
<td></td>
<td></td>
</tr>
<tr>
<td>207</td>
<td>6</td>
<td>71</td>
<td>268</td>
<td>461</td>
<td>0</td>
<td>59</td>
<td></td>
<td></td>
</tr>
<tr>
<td>207</td>
<td>7</td>
<td>75</td>
<td>189</td>
<td>712</td>
<td>1</td>
<td>57</td>
<td></td>
<td></td>
</tr>
<tr>
<td>207</td>
<td>8</td>
<td>94</td>
<td>208</td>
<td>982</td>
<td>0</td>
<td>82</td>
<td></td>
<td></td>
</tr>
<tr>
<td>207</td>
<td>9</td>
<td>54</td>
<td>153</td>
<td>932</td>
<td>0</td>
<td>63</td>
<td></td>
<td></td>
</tr>
<tr>
<td>207</td>
<td>10</td>
<td>68</td>
<td>156</td>
<td>991</td>
<td>0</td>
<td>51</td>
<td></td>
<td></td>
</tr>
<tr>
<td>207</td>
<td>11</td>
<td>103</td>
<td>174</td>
<td>949</td>
<td>0</td>
<td>89</td>
<td></td>
<td></td>
</tr>
<tr>
<td>207</td>
<td>12</td>
<td>111</td>
<td>138</td>
<td>616</td>
<td>0</td>
<td>90</td>
<td></td>
<td></td>
</tr>
<tr>
<td>207</td>
<td>13</td>
<td>111</td>
<td>185</td>
<td>867</td>
<td>0</td>
<td>85</td>
<td></td>
<td></td>
</tr>
<tr>
<td>207</td>
<td>14</td>
<td>44</td>
<td>343</td>
<td>358</td>
<td>0</td>
<td>54</td>
<td></td>
<td></td>
</tr>
</tbody></table>
<table>
<thead>
<tr>
<th>173</th>
<th>1</th>
<th>6</th>
<th>3645</th>
<th>41</th>
<th>0</th>
<th>4</th>
<th>1</th>
<th>25</th>
</tr>
</thead>
<tbody><tr>
<td>173</td>
<td>2</td>
<td>10</td>
<td>6873</td>
<td>39</td>
<td>0</td>
<td>5</td>
<td></td>
<td></td>
</tr>
<tr>
<td>173</td>
<td>3</td>
<td>8</td>
<td>6508</td>
<td>39</td>
<td>0</td>
<td>5</td>
<td></td>
<td></td>
</tr>
<tr>
<td>173</td>
<td>4</td>
<td>21</td>
<td>1906</td>
<td>62</td>
<td>1</td>
<td>13</td>
<td></td>
<td></td>
</tr>
<tr>
<td>173</td>
<td>5</td>
<td>20</td>
<td>103</td>
<td>44</td>
<td>0</td>
<td>12</td>
<td></td>
<td></td>
</tr>
<tr>
<td>173</td>
<td>6</td>
<td>19</td>
<td>102</td>
<td>41</td>
<td>0</td>
<td>14</td>
<td></td>
<td></td>
</tr>
<tr>
<td>173</td>
<td>7</td>
<td>12</td>
<td>91</td>
<td>55</td>
<td>0</td>
<td>9</td>
<td></td>
<td></td>
</tr>
<tr>
<td>173</td>
<td>8</td>
<td>12</td>
<td>81</td>
<td>137</td>
<td>1</td>
<td>11</td>
<td></td>
<td></td>
</tr>
<tr>
<td>173</td>
<td>9</td>
<td>20</td>
<td>869</td>
<td>217</td>
<td>1</td>
<td>13</td>
<td></td>
<td></td>
</tr>
<tr>
<td>173</td>
<td>10</td>
<td>10</td>
<td>107</td>
<td>136</td>
<td>0</td>
<td>6</td>
<td></td>
<td></td>
</tr>
<tr>
<td>173</td>
<td>11</td>
<td>18</td>
<td>117</td>
<td>90</td>
<td>0</td>
<td>12</td>
<td></td>
<td></td>
</tr>
<tr>
<td>173</td>
<td>12</td>
<td>14</td>
<td>133</td>
<td>458</td>
<td>1</td>
<td>10</td>
<td></td>
<td></td>
</tr>
<tr>
<td>173</td>
<td>13</td>
<td>15</td>
<td>878</td>
<td>2711</td>
<td>1</td>
<td>9</td>
<td></td>
<td></td>
</tr>
<tr>
<td>173</td>
<td>14</td>
<td>16</td>
<td>247</td>
<td>197</td>
<td>0</td>
<td>13</td>
<td></td>
<td></td>
</tr>
</tbody></table>
<p>生成数据+在原有基础上生成数据</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br><span class="line">94</span><br><span class="line">95</span><br><span class="line">96</span><br><span class="line">97</span><br><span class="line">98</span><br><span class="line">99</span><br><span class="line">100</span><br><span class="line">101</span><br><span class="line">102</span><br><span class="line">103</span><br><span class="line">104</span><br><span class="line">105</span><br><span class="line">106</span><br><span class="line">107</span><br><span class="line">108</span><br><span class="line">109</span><br><span class="line">110</span><br><span class="line">111</span><br><span class="line">112</span><br><span class="line">113</span><br><span class="line">114</span><br><span class="line">115</span><br><span class="line">116</span><br><span class="line">117</span><br><span class="line">118</span><br><span class="line">119</span><br><span class="line">120</span><br><span class="line">121</span><br><span class="line">122</span><br><span class="line">123</span><br><span class="line">124</span><br><span class="line">125</span><br><span class="line">126</span><br><span class="line">127</span><br><span class="line">128</span><br><span class="line">129</span><br><span class="line">130</span><br><span class="line">131</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/7/27</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> random</span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">data = pd.read_excel(<span class="string">&#x27;data1&#x27;</span> + <span class="string">&#x27;.xlsx&#x27;</span>)</span><br><span class="line">data.fillna(<span class="number">0</span>, inplace=<span class="literal">True</span>)</span><br><span class="line"><span class="comment"># 转换列表</span></span><br><span class="line">list_data = np.array(data).tolist()</span><br><span class="line">list_i1 = [<span class="built_in">int</span>(list_data[i][<span class="number">0</span>]) <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data))]</span><br><span class="line">list_t1 = [<span class="built_in">int</span>(list_data[i][<span class="number">1</span>]) <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data))]</span><br><span class="line"></span><br><span class="line">list_y1 = [<span class="built_in">int</span>(list_data[i][<span class="number">2</span>]) <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data))]</span><br><span class="line">list_x1 = [<span class="built_in">int</span>(list_data[i][<span class="number">3</span>]) <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data))]</span><br><span class="line">list_x2 = [<span class="built_in">int</span>(list_data[i][<span class="number">4</span>]) <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data))]</span><br><span class="line">list_x3 = [<span class="built_in">int</span>(list_data[i][<span class="number">5</span>]) <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data))]</span><br><span class="line">list_x4 = [<span class="built_in">int</span>(list_data[i][<span class="number">6</span>]) <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data))]</span><br><span class="line"></span><br><span class="line">list_z1 = [<span class="built_in">int</span>(list_data[i][<span class="number">7</span>]) <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data))]</span><br><span class="line">list_z2 = [<span class="built_in">int</span>(list_data[i][<span class="number">8</span>]) <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data))]</span><br><span class="line"></span><br><span class="line">y1_mean = np.array(list_y1).mean()</span><br><span class="line">y1_std = np.array(list_y1).std()</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">14</span>*<span class="number">200</span>):</span><br><span class="line">    list_y1.append(random.randint(<span class="number">0</span>, <span class="number">10</span>))</span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">14</span>*<span class="number">50</span>):</span><br><span class="line">    list_y1.append(random.randint(<span class="number">3</span>, <span class="number">25</span>))</span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">14</span>*<span class="number">80</span>):</span><br><span class="line">    list_y1.append(random.randint(<span class="number">15</span>, <span class="number">50</span>))</span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">14</span>*<span class="number">50</span>):</span><br><span class="line">    list_y1.append(random.randint(<span class="number">35</span>, <span class="number">120</span>))</span><br><span class="line"></span><br><span class="line">list_z_y = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">int</span>(<span class="built_in">len</span>(list_y1)/<span class="number">14</span>)):</span><br><span class="line">    y1 = []</span><br><span class="line">    <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(list_y1)):</span><br><span class="line">        <span class="keyword">if</span> <span class="number">14</span> * i &lt;= j &lt; <span class="number">14</span> * (i + <span class="number">1</span>):</span><br><span class="line">            y1.append(list_y1[j])</span><br><span class="line">    list_z_y.append(np.array(y1).mean())</span><br><span class="line"></span><br><span class="line">Y_Z = []</span><br><span class="line">num1 = <span class="number">0</span></span><br><span class="line">num2 = <span class="number">0</span></span><br><span class="line">num3 = <span class="number">0</span></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(list_z_y)):</span><br><span class="line">    <span class="keyword">if</span> list_z_y[i] &lt; <span class="number">1</span>:</span><br><span class="line">        Y_Z.append(<span class="number">0</span>)</span><br><span class="line">        num1 += <span class="number">1</span></span><br><span class="line">    <span class="keyword">elif</span> <span class="number">1</span> &lt;= list_z_y[i] &lt; np.array(list_y1).std():</span><br><span class="line">        Y_Z.append(<span class="number">1</span>)</span><br><span class="line">        num2 += <span class="number">1</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        Y_Z.append(<span class="number">2</span>)</span><br><span class="line">        num3 += <span class="number">1</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 将Y_Z变成字典形式</span></span><br><span class="line">dict_Y_Z = <span class="built_in">dict</span>(<span class="built_in">zip</span>(np.arange(<span class="built_in">len</span>(Y_Z)).tolist(), Y_Z))</span><br><span class="line">sort_dict = <span class="built_in">sorted</span>(<span class="built_in">zip</span>(dict_Y_Z.values(), dict_Y_Z.keys()))</span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data), <span class="built_in">len</span>(list_y1)):</span><br><span class="line">    <span class="keyword">if</span> list_y1[i] &lt; <span class="number">1</span>:</span><br><span class="line">        list_x1.append(random.randint(<span class="number">1</span>, <span class="number">2000</span>))</span><br><span class="line">        list_x2.append(random.randint(<span class="number">1</span>, <span class="number">1000</span>))</span><br><span class="line">        list_x3.append(random.randint(<span class="number">0</span>, <span class="number">1</span>))</span><br><span class="line">        list_x4.append(random.randint(<span class="number">0</span>, <span class="number">2</span>))</span><br><span class="line">    <span class="keyword">elif</span> <span class="number">1</span> &lt;= list_y1[i] &lt; np.array(list_y1).std():</span><br><span class="line">        list_x1.append(random.randint(<span class="number">200</span>, <span class="number">5000</span>))</span><br><span class="line">        list_x2.append(random.randint(<span class="number">200</span>, <span class="number">2000</span>))</span><br><span class="line">        list_x3.append(random.randint(<span class="number">0</span>, <span class="number">10</span>))</span><br><span class="line">        list_x4.append(random.randint(<span class="number">5</span>, <span class="number">20</span>))</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        list_x1.append(random.randint(<span class="number">2000</span>, <span class="number">10000</span>))</span><br><span class="line">        list_x2.append(random.randint(<span class="number">1000</span>, <span class="number">5000</span>))</span><br><span class="line">        list_x3.append(random.randint(<span class="number">2</span>, <span class="number">30</span>))</span><br><span class="line">        list_x4.append(random.randint(<span class="number">10</span>, <span class="number">100</span>))</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data), <span class="built_in">len</span>(list_y1)):</span><br><span class="line">    <span class="keyword">if</span> i % <span class="number">14</span> == <span class="number">0</span>:</span><br><span class="line">        <span class="keyword">if</span> Y_Z[<span class="built_in">int</span>(i/<span class="number">14</span>)] == <span class="number">0</span>:</span><br><span class="line">            list_z1.append(random.randint(<span class="number">0</span>, <span class="number">9</span>))</span><br><span class="line">            list_z2.append(random.randint(<span class="number">0</span>, <span class="number">9</span>))</span><br><span class="line">        <span class="keyword">elif</span> Y_Z[<span class="built_in">int</span>(i/<span class="number">14</span>)] == <span class="number">1</span>:</span><br><span class="line">            list_z1.append(random.randint(<span class="number">0</span>, <span class="number">9</span>))</span><br><span class="line">            list_z2.append(random.randint(<span class="number">5</span>, <span class="number">100</span>))</span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            list_z1.append(random.randint(<span class="number">0</span>, <span class="number">9</span>))</span><br><span class="line">            list_z2.append(random.randint(<span class="number">50</span>, <span class="number">500</span>))</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        list_z1.append(<span class="number">0</span>)</span><br><span class="line">        list_z2.append(<span class="number">0</span>)</span><br><span class="line"></span><br><span class="line">num_i1 = <span class="number">231</span></span><br><span class="line"><span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">int</span>(<span class="built_in">len</span>(data)/<span class="number">14</span>), <span class="built_in">int</span>(<span class="built_in">len</span>(list_y1)/<span class="number">14</span>)):</span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data), <span class="built_in">len</span>(list_y1)):</span><br><span class="line">        <span class="keyword">if</span> <span class="number">14</span> * j &lt;= i &lt; <span class="number">14</span> * (j + <span class="number">1</span>):</span><br><span class="line">            list_i1.append(num_i1)</span><br><span class="line">    num_i1 += <span class="number">1</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">int</span>(<span class="built_in">len</span>(data)/<span class="number">14</span>), <span class="built_in">int</span>(<span class="built_in">len</span>(list_y1)/<span class="number">14</span>)):</span><br><span class="line">    k = <span class="number">0</span></span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data), <span class="built_in">len</span>(list_y1)):</span><br><span class="line">        <span class="keyword">if</span> <span class="number">14</span> * j &lt;= i &lt; <span class="number">14</span> * (j + <span class="number">1</span>):</span><br><span class="line">            k = k + <span class="number">1</span></span><br><span class="line">            list_t1.append(k)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">df_i1 = pd.DataFrame(list_i1, columns=[<span class="string">&#x27;i&#x27;</span>])</span><br><span class="line">df_t1 = pd.DataFrame(list_t1, columns=[<span class="string">&#x27;t&#x27;</span>])</span><br><span class="line"></span><br><span class="line">df_y1 = pd.DataFrame(list_y1, columns=[<span class="string">&#x27;y&#x27;</span>])</span><br><span class="line">df_x1 = pd.DataFrame(list_x1, columns=[<span class="string">&#x27;x1&#x27;</span>])</span><br><span class="line">df_x2 = pd.DataFrame(list_x2, columns=[<span class="string">&#x27;x2&#x27;</span>])</span><br><span class="line">df_x3 = pd.DataFrame(list_x3, columns=[<span class="string">&#x27;x3&#x27;</span>])</span><br><span class="line">df_x4 = pd.DataFrame(list_x4, columns=[<span class="string">&#x27;x4&#x27;</span>])</span><br><span class="line">df_z1 = pd.DataFrame(list_z1, columns=[<span class="string">&#x27;z1&#x27;</span>])</span><br><span class="line">df_z2 = pd.DataFrame(list_z2, columns=[<span class="string">&#x27;z2&#x27;</span>])</span><br><span class="line">all_data = pd.concat([df_i1, df_t1, df_y1, df_x1, df_x2, df_x3, df_x4, df_z1, df_z2], axis=<span class="number">1</span>)</span><br><span class="line"></span><br><span class="line">all_data.to_excel(<span class="string">&#x27;all_data.xlsx&#x27;</span>)</span><br><span class="line"></span><br></pre></td></tr></table></figure>

<p>此时对应的参数设置</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br></pre></td><td class="code"><pre><span class="line">百次循环后，最高准确率<span class="number">0.9383155397390273</span></span><br><span class="line">最大准确率对应的序号：<span class="number">50</span></span><br><span class="line">百次循环后，似然值为<span class="number">-4063.265863470829</span></span><br><span class="line">对应的BIC为<span class="number">-4195.540439027526</span></span><br><span class="line">在<span class="number">4</span>个变量下的状态转移概率矩阵：[[<span class="number">0.9963942307692307</span>, <span class="number">0.003605769230769231</span>, <span class="number">0</span>], [<span class="number">0.10307564422277639</span>, <span class="number">0.8969243557772236</span>, <span class="number">0.0</span>], [<span class="number">0.0</span>, <span class="number">0.058704453441295545</span>, <span class="number">0.9412955465587044</span>]]</span><br><span class="line">--------------------</span><br><span class="line">参数估计</span><br><span class="line">状态转移概率矩阵的系数为：[[ <span class="number">-0.12383302</span>  <span class="number">-0.05998412</span>  <span class="number">-0.24795679</span> <span class="number">-14.57070714</span>]</span><br><span class="line"> [  <span class="number">0.0197879</span>   <span class="number">-0.05702528</span>   <span class="number">0.04541117</span>  <span class="number">-0.50336113</span>]</span><br><span class="line"> [  <span class="number">1.42194922</span>   <span class="number">0.30341754</span>   <span class="number">1.19921318</span>   <span class="number">7.34666875</span>]]</span><br><span class="line">状态转移概率矩阵的偏差为：[<span class="number">-8.58771943</span> <span class="number">-0.06000747</span> <span class="number">-3.32333259</span>]</span><br><span class="line">观察状态概率转移矩阵的系数为：[[<span class="number">-1.01971402</span> <span class="number">-0.57938656</span>]</span><br><span class="line"> [ <span class="number">0.83284649</span> <span class="number">-1.75792799</span>]</span><br><span class="line"> [ <span class="number">0.25329582</span>  <span class="number">2.1562813</span> ]]</span><br><span class="line">观察状态概率转移矩阵的偏差为：[<span class="number">-1.02302946</span> <span class="number">-0.42107895</span> <span class="number">-1.67846306</span>]</span><br><span class="line">变量<span class="number">1</span>下的知识贡献意愿转移概率</span><br><span class="line">[[<span class="number">0.904165130851456</span>, <span class="number">0.09583486914854404</span>, <span class="number">0</span>], [<span class="number">0.8882108860141567</span>, <span class="number">0.0976324139614352</span>, <span class="number">0.014156700024408104</span>], [<span class="number">0</span>, <span class="number">0.009270704573547589</span>, <span class="number">0.9907292954264524</span>]]</span><br><span class="line">变量<span class="number">2</span>下的知识贡献意愿转移概率</span><br><span class="line">[[<span class="number">0.8673055657943236</span>, <span class="number">0.13269443420567636</span>, <span class="number">0</span>], [<span class="number">0.877959482548206</span>, <span class="number">0.1098364657066146</span>, <span class="number">0.0122040517451794</span>], [<span class="number">0</span>, <span class="number">0.009888751545117428</span>, <span class="number">0.9901112484548825</span>]]</span><br><span class="line">变量<span class="number">3</span>下的知识贡献意愿转移概率</span><br><span class="line">[[<span class="number">0.8669369701437523</span>, <span class="number">0.13306302985624768</span>, <span class="number">0</span>], [<span class="number">0.8777154015133024</span>, <span class="number">0.11008054674151818</span>, <span class="number">0.0122040517451794</span>], [<span class="number">0</span>, <span class="number">0.007416563658838072</span>, <span class="number">0.992583436341162</span>]]</span><br><span class="line">变量<span class="number">4</span>下的知识贡献意愿转移概率</span><br><span class="line">[[<span class="number">0.8669369701437523</span>, <span class="number">0.13306302985624768</span>, <span class="number">0</span>], [<span class="number">0.8777154015133024</span>, <span class="number">0.11008054674151818</span>, <span class="number">0.0122040517451794</span>], [<span class="number">0</span>, <span class="number">0.007416563658838072</span>, <span class="number">0.992583436341162</span>]]</span><br><span class="line">全部变量下的知识贡献意愿转移概率</span><br><span class="line">u1=<span class="number">-8.537239946774692</span>,u2_1=<span class="number">-0.11583742328882626</span>,u2_h=<span class="number">-0.7220150302930365</span>, u3=<span class="number">-0.4933279734329048</span></span><br><span class="line">状态转移概率矩阵的系数为：[[ <span class="number">-0.12383302</span>  <span class="number">-0.05998412</span>  <span class="number">-0.24795679</span> <span class="number">-14.57070714</span>]</span><br><span class="line"> [  <span class="number">0.0197879</span>   <span class="number">-0.05702528</span>   <span class="number">0.04541117</span>  <span class="number">-0.50336113</span>]</span><br><span class="line"> [  <span class="number">1.42194922</span>   <span class="number">0.30341754</span>   <span class="number">1.19921318</span>   <span class="number">7.34666875</span>]]</span><br><span class="line">观察状态概率转移矩阵的系数为：[[<span class="number">-1.01971402</span> <span class="number">-0.57938656</span>]</span><br><span class="line"> [ <span class="number">0.83284649</span> <span class="number">-1.75792799</span>]</span><br><span class="line"> [ <span class="number">0.25329582</span>  <span class="number">2.1562813</span> ]]</span><br><span class="line">观察状态概率转移矩阵的截距为：[<span class="number">-1.02302946</span> <span class="number">-0.42107895</span> <span class="number">-1.67846306</span>]</span><br><span class="line">所有参数和矩阵表格如上</span><br><span class="line">w1(x1)标准偏差为：<span class="number">0.02525464573099962</span></span><br><span class="line">w1(x2)标准偏差为：<span class="number">0.026393311062668005</span></span><br><span class="line">w1(x3)标准偏差为：<span class="number">0.07533481875592112</span></span><br><span class="line">w1(x4)标准偏差为：<span class="number">0.13181323299672285</span></span><br><span class="line">w2(x1)标准偏差为：<span class="number">0.01959329097587093</span></span><br><span class="line">w2(x2)标准偏差为：<span class="number">0.015565458670391606</span></span><br><span class="line">w2(x3)标准偏差为：<span class="number">0.01844469465856866</span></span><br><span class="line">w2(x4)标准偏差为：<span class="number">0.017210850554842068</span></span><br><span class="line">w3(x1)标准偏差为：<span class="number">0.08027460747756567</span></span><br><span class="line">w3(x2)标准偏差为：<span class="number">0.1133582520346092</span></span><br><span class="line">w3(x3)标准偏差为：<span class="number">0.09188332111057886</span></span><br><span class="line">w3(x4)标准偏差为：<span class="number">0.1810153307790173</span></span><br><span class="line">w1(Z1)标准偏差为：<span class="number">0.0722882365013184</span></span><br><span class="line">w1(Z2)标准偏差为：<span class="number">0.12683764260300465</span></span><br><span class="line">w2(Z1)标准偏差为：<span class="number">0.0722882365013184</span></span><br><span class="line">w2(Z2)标准偏差为：<span class="number">0.12683764260300465</span></span><br><span class="line">w3(Z1)标准偏差为：<span class="number">0.0722882365013184</span></span><br><span class="line">w3(Z2)标准偏差为：<span class="number">0.12683764260300465</span></span><br><span class="line">bz1截距的标准偏差为：<span class="number">0.07779008201467114</span></span><br><span class="line">bz2截距的标准偏差为：<span class="number">0.0651521267497443</span></span><br><span class="line">bz3截距的标准偏差为：<span class="number">0.08229776007120924</span></span><br><span class="line">bx1阈值的标准偏差为：<span class="number">0.05107259495619836</span></span><br><span class="line">bx2阈值的标准偏差为：<span class="number">0.015587987616212207</span></span><br><span class="line">bx3阈值的标准偏差为：<span class="number">0.06441769065231308</span></span><br></pre></td></tr></table></figure>

<p><strong>直接生成数据</strong></p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">math.log(<span class="number">3.9118942731277535</span>)</span><br><span class="line">Out[<span class="number">5</span>]: <span class="number">1.3640217255107674</span></span><br><span class="line"><span class="number">1.3640217255107674</span>+<span class="number">5.4588414</span></span><br><span class="line">Out[<span class="number">6</span>]: <span class="number">6.822863125510767</span></span><br></pre></td></tr></table></figure>


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